Matches in SemOpenAlex for { <https://semopenalex.org/work/W3210402436> ?p ?o ?g. }
- W3210402436 endingPage "398" @default.
- W3210402436 startingPage "383" @default.
- W3210402436 abstract "Reliable modeling of river sediments transport is important as it is a defining factor of the economic viability of dams, the durability of hydroelectric-equipment, river susceptibility to pollution, suitability for navigation, and potential for aesthetics and fish habitat. The capability of a new machine learning model, fuzzy c-means based neuro-fuzzy system calibrated using the hybrid particle swarm optimization-gravitational search algorithm (ANFIS-FCM-PSOGSA) in improving the estimation accuracy of river suspended sediment loads (SSLs) is investigated in the current study. The outcomes of the proposed method were compared with those obtained using the fuzzy c-means based neuro-fuzzy system calibrated using particle swarm optimization (ANFIS-FCM-PSO), ANFIS-FCM, and sediment rating curve (SRC) models. Various input combinations involving lagged river flow (Q) and suspended sediment (S) values were used for model development. The effect of Q and S on the model's accuracy also was assessed by including the difference between lagged Q and S values as inputs. The model performance was assessed using the root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe Efficiency (NSE), and coefficient of determination (R2) and several graphical comparison methods. The results showed that the proposed model enhanced the prediction performance of the ANFIS-FCM-PSO (or ANFIS-FCM) models by 8.14% (1.72%), 14.7% (5.71%), 12.5% (2.27%), and 25.6% (1.86%), in terms of the RMSE, MAE, NSE and R2, respectively. The current study established the potential of the proposed ANFIS-FCM-PSOGSA model for simulation of the cumulative sediment load. The modeling results revealed the potential effects of the river flow lags on the sediment transport quantification." @default.
- W3210402436 created "2021-11-08" @default.
- W3210402436 creator A5001907851 @default.
- W3210402436 creator A5016315589 @default.
- W3210402436 creator A5022052919 @default.
- W3210402436 creator A5037953109 @default.
- W3210402436 creator A5052523726 @default.
- W3210402436 creator A5057749312 @default.
- W3210402436 date "2022-06-01" @default.
- W3210402436 modified "2023-10-18" @default.
- W3210402436 title "Predictability performance enhancement for suspended sediment in rivers: Inspection of newly developed hybrid adaptive neuro-fuzzy system model" @default.
- W3210402436 cites W1988523040 @default.
- W3210402436 cites W2003623399 @default.
- W3210402436 cites W2019207321 @default.
- W3210402436 cites W2021290128 @default.
- W3210402436 cites W2027388149 @default.
- W3210402436 cites W2028745710 @default.
- W3210402436 cites W2036354737 @default.
- W3210402436 cites W2050546928 @default.
- W3210402436 cites W2057309892 @default.
- W3210402436 cites W2061435454 @default.
- W3210402436 cites W2062236565 @default.
- W3210402436 cites W2063081170 @default.
- W3210402436 cites W2079629052 @default.
- W3210402436 cites W2095356761 @default.
- W3210402436 cites W2106595237 @default.
- W3210402436 cites W2294619149 @default.
- W3210402436 cites W2336021816 @default.
- W3210402436 cites W2561342315 @default.
- W3210402436 cites W2568902225 @default.
- W3210402436 cites W2586766860 @default.
- W3210402436 cites W2608783221 @default.
- W3210402436 cites W2763383283 @default.
- W3210402436 cites W2765286064 @default.
- W3210402436 cites W2768272937 @default.
- W3210402436 cites W2770693717 @default.
- W3210402436 cites W2791995454 @default.
- W3210402436 cites W2793974136 @default.
- W3210402436 cites W2795103316 @default.
- W3210402436 cites W2795470013 @default.
- W3210402436 cites W2801536506 @default.
- W3210402436 cites W2802958163 @default.
- W3210402436 cites W2804523998 @default.
- W3210402436 cites W2807276156 @default.
- W3210402436 cites W2890516898 @default.
- W3210402436 cites W2895064848 @default.
- W3210402436 cites W2895211850 @default.
- W3210402436 cites W2897565907 @default.
- W3210402436 cites W2898163147 @default.
- W3210402436 cites W2903425314 @default.
- W3210402436 cites W2904147223 @default.
- W3210402436 cites W2904672395 @default.
- W3210402436 cites W2911491231 @default.
- W3210402436 cites W2938010697 @default.
- W3210402436 cites W2944532009 @default.
- W3210402436 cites W2951791429 @default.
- W3210402436 cites W2967948338 @default.
- W3210402436 cites W2972093761 @default.
- W3210402436 cites W2972778482 @default.
- W3210402436 cites W2973373886 @default.
- W3210402436 cites W2977306776 @default.
- W3210402436 cites W2978560988 @default.
- W3210402436 cites W2980655104 @default.
- W3210402436 cites W2988714259 @default.
- W3210402436 cites W2989877241 @default.
- W3210402436 cites W2990109322 @default.
- W3210402436 cites W2990513038 @default.
- W3210402436 cites W2996724567 @default.
- W3210402436 cites W3007705148 @default.
- W3210402436 cites W3007877061 @default.
- W3210402436 cites W3014411954 @default.
- W3210402436 cites W3015759958 @default.
- W3210402436 cites W3021553320 @default.
- W3210402436 cites W3022000484 @default.
- W3210402436 cites W3026781913 @default.
- W3210402436 cites W3027953868 @default.
- W3210402436 cites W3029309055 @default.
- W3210402436 cites W3030428089 @default.
- W3210402436 cites W3031322214 @default.
- W3210402436 cites W3032255525 @default.
- W3210402436 cites W3037312783 @default.
- W3210402436 cites W3040507935 @default.
- W3210402436 cites W3048356388 @default.
- W3210402436 cites W3081270234 @default.
- W3210402436 cites W3085019631 @default.
- W3210402436 cites W3094878791 @default.
- W3210402436 cites W3096893347 @default.
- W3210402436 cites W3105351184 @default.
- W3210402436 cites W3113018525 @default.
- W3210402436 cites W3127301485 @default.
- W3210402436 cites W3158966585 @default.
- W3210402436 cites W3159598116 @default.
- W3210402436 cites W3161470247 @default.
- W3210402436 cites W3201565706 @default.
- W3210402436 doi "https://doi.org/10.1016/j.ijsrc.2021.10.001" @default.
- W3210402436 hasPublicationYear "2022" @default.
- W3210402436 type Work @default.
- W3210402436 sameAs 3210402436 @default.